Feature Fusion Model Using Transfer Learning and Bidirectional Attention Mechanism for Plant Pipeline Leak Detection - Yujin Han; Yourak Choi; Jonghyuk Lee; Ji-Hoon Bae (Applied Sciences)
The aging of power plant pipelines has led to significant leaks worldwide, causing environmental damage, human safety risks, and economic losses. Rapid leak detection is critical for mitigating these issues, but challenges such as varying leak characteristics, ambient noise, and limited real-world data complicate their accurate detection and model development. To address these issues, we propose a leak detection model that integrates stepwise transfer learning and an attention mechanism. The proposed model utilizes a two-stage deep learning process. In Stage 1, one-dimensional convolutional neural networks (1D CNNs) are pre-trained to extract root mean square (RMS) and frequency-domain features from acoustic signals. In Stage 2, the classifier layers of the pre-trained models are removed, and the extracted features are fused and processed using a bidirectional long short-term memory (LSTM) network. An attention mechanism is incorporated within the LSTM to prioritize critical features, enhancing the ability of the model to distinguish leak signals from noise. The model achieved an accuracy of 99.99%, significantly outperforming the traditional methods considered in this study. By effectively addressing noise interference and data scarcity, this robust approach demonstrates its potential to enhance safety, reduce risks, and improve cost efficiency in industrial pipelines and critical infrastructure.